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040			_aEG-GiCUC _cEG-GiCUC _beng
041	0		_aeng _beng _bara
049			_aDeposite
082	0	4	_a600
092			_a600 _221
097			_aM.Sc
099			_aCai01.20.04.M.Sc.2022.Di.I
100	0		_aDina Mohamed Kamal Atito _epreparation.
245	1	0	_aImproving recommendation systems using semantic technologies / _cDina Mohamed Kamal Atito ; Supervised Hoda Mokhtar Omar Mokhtar , Ayman Ramadan Elkilany.
246	1	5	_aتحسين أنظمة التوصية بإستخدام التكنولوجيا الدلالية
264		0	_c 2022
300			_a65 pages : _billustrations ; _c30 cm+ _eCD
336			_atext _2rda content
337			_aUnmediated _2rdamedia
338			_avolume _2rdacarrier
502			_aThesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial intelligence - Department of Information Systems
504			_aBibliography: pages 69-75.
520		3	_aRecommendation systems are algorithms that aim to predict the users' needs and automatically suggest the most relevant items to the users. Recommender systems are becoming increasingly popular in our daily lives and applied in different domains to facilitate finding relevant and interesting items to the users. In the academic domain, the academic article recommendation systems have gained a lot of interest as an effective tool to suggest relevant articles for researchers according to their interests. An explicit identification of the topics of interest from the contents of academic articles that the researchers have authored, downloaded, or read has been always a challenging task. Accurate articles recommendation relies on the true identification of researchers{u2018} interests which is affected by the quality of the article's textual representation. In this thesis, we aim to improve the results of the academic recommendation system by enhancing the representation of the article and consequently enhancing the quality of the recommendation. In order to improve the representation of the articles, we focus on the semantic approaches to represent the words' semantic meanings rather than their syntactic representation only. In this thesis, two semantic representation models are proposed for articles representation, both models have been applied in the academic articles recommendation process
530			_aIssued also as CD
546			_aText in English and abstract in Arabic & English.
650		0	_aLDA
653		4	_aLatent Dirichlet Allocation(LDA) _aRecommendation Systems _aWord2vec
700	0		_aAyman Ramadan Elkilany _ethesis advisor.
700	0		_aHoda Mokhtar Omar Mokhtar _ethesis advisor.
900			_b01-01-2022 _cHoda Mokhtar Omar Mokhtar _cAyman Ramadan Elkilany _UCairo University _FFaculty of Computers and Artificial intelligence _DDepartment of Information Systems
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c84293